Background New-onset diabetes after transplantation (NODAT) is a major post-transplant complication associated with lower allograft and recipient survival. Our objective was to determine if metabolic syndrome pre-transplant is independently associated with NODAT development. Methods We recruited 640 consecutive incident non-diabetic renal transplant recipients from 3 academic centers between 1999 and 2004. NODAT was defined as use of hypoglycemic medication, a random plasma glucose >200 mg/dL, or 2 fasting glucose levels ≥126 mg/dL beyond 30 days post-transplant. Results Metabolic syndrome was common pre-transplant (57.2 %). NODAT developed in 31.4% of recipients one year post-transplant. Participants with metabolic syndrome were more likely to develop NODAT compared to recipients without metabolic syndrome (34.4% v. 27.4%, p=0.057). Recipients with increasing number of positive metabolic syndrome components were more likely to develop NODAT (metabolic syndrome score-prevalence at 1 year: 0-0.0%, 1-24.2, 2-29.3%, 3-31.0%, 4-34.8%, and 5-73.7%, p=0.001). After adjustment for demographics, age by decade (HR-1.34 (1.20-1.50), p<0.0001), African American race (HR-1.35 (1.01-1.82), p=0.043), cumulative prednisone dosage (HR-1.18 (1.07-1.30), p=0.001), and metabolic syndrome (HR-1.34 (1.00-1.79), p=0.047) were independent predictors of development of NODAT at 1 year post-transplant. In a multivariable analysis incorporating the individual metabolic syndrome components themselves as covariates, the only pre-transplant metabolic syndrome component to remain an independent predictor of NODAT was low HDL (HR-1.37 (1.01-1.85), p=0.042). Conclusions Metabolic syndrome is an independent predictor for NODAT and is a possible target for intervention to prevent NODAT. Future studies to evaluate if modification of metabolic syndrome factors pre-transplant reduces NODAT development are needed.

Background: Low physical activity (PA) has been associated with higher rates of cardiovascular disease (CVD) and mortality in the general population. Despite the benefits of kidney transplantation, kidney transplant recipients (KTRs) remain at elevated risk for CVD and mortality compared to individuals without kidney disease. Methods: A prospective cohort of 507 adult KTRs from three academic centers completed the Physical Activity Scale for the Elderly (PASE) at transplantation. PASE scores were divided into tertiles. Results: PA was lower with older age, history of CVD, smoking, and diabetes. During the median 8-year follow-up period, 128 individuals died, among whom 101 had a functioning allograft. In multivariable Cox regression for all-cause mortality, greater PA was strongly associated with better survival (HR: 0.52 for most active vs. inactive tertiles, 95% CI: 0.31–0.87, p = 0.01). Secondary analyses, in which (1) death with a functioning graft was the primary outcome, and (2) PASE scores were converted to the metabolic equivalent of task, revealed similar results. We did not find an association between change of PA after transplantation and mortality. Conclusions: PA at the time of kidney transplantation is a strong predictor of all-cause mortality and death with graft function. Evaluation of PA level among kidney transplant candidates may be a useful method to risk-stratify patients for survival after kidney transplantation. Kidney transplant candidates and recipients should also be encouraged to be physically active.

Purpose: Claims databases offer large populations for research, but lack clinical details. We aimed to develop predictive models to identify estrogen receptor positive (ER+) and human epidermal growth factor negative (HER2−) early breast cancer (ESBC) and advanced stage breast cancer (ASBC) in a claims database. Methods: Female breast cancer cases in Anthem's Cancer Care Quality Program served as the gold standard validation sample. Predictive models were developed from clinical knowledge and empirically from claims data using logistic and lasso regression. Model performance was assessed by classification rates and c-statistics. Models were applied to the HealthCore Integrated Research Database (claims) to identify cohorts of women with ER+/HER2− ESBC and ASBC. Results: The validation sample included 3184 women with ER+/HER2− ESBC and 1436 with ER+/HER2− ASBC. Predictive models for ER+/HER2− ESBC and ASBC included 25 and 20 factors, respectively. Models had robust discrimination in identifying cases (c-stat = 0.92 for ESBC and 0.95 for ASBC). Compared with a traditional a priori algorithm developed with clinical insight alone, the ER+/HER2− ASBC-predictive model had better positive predictive value (PPV) (0.91, 95% CI, 0.90-0.93, vs 0.69, 95%CI, 0.66-0.73) and sensitivity (0.54 vs 0.35). Models were applied to the claims database to identify cohorts of 33 001 and 3198 women with ER+/HER2− ESBC and ASBC. Conclusion:We conducted a validation study and developed predictive models to identify in a claims database cohorts of women with ER+/HER2− ESBC and ASBC.The models identified large cohorts in the claims data that can be used to characterize indications in the evaluation of targeted therapies.

Background: We conducted a study to assess whether testosterone therapy (TT) alters prostate cancer risk using a large U.S. commercial insurance research database.Methods: From the HealthCore Integrated Research Database (HIRD), we selected men ages 30 years or greater who were new users of TT during 2007 to 2015. We selected two comparison groups: (i) unexposed (matched 10:1) and (ii) new users of phosphodiesterase type 5 inhibitor (PDE5i). Incident prostate cancer was defined as diagnosis of prostate cancer within 4 weeks following prostate biopsy. Propensity scores and inverse probability of treatment weights were used in Poisson regression models to estimate adjusted incidence rates, incidence rate ratios (IRR), and 95% confidence intervals (CI). Subgroup analyses included stratification by prostate cancer screening, hypogonadism, and follow-up time.Results: The adjusted prostate cancer IRR was 0.77 (95% CI, 0.68-0.86) when comparing TT with the unexposed group and 0.85 (95% CI, 0.79-0.91) in comparison with the PDE5i group. Inverse associations between TT and prostate cancer were observed in a majority of subgroup analyses, although in both comparisons estimates generally attenuated with increasing time following initial exposure. Among TT users, duration of exposure was not associated with prostate cancer.Conclusions: Men who received TT did not have a higher rate of prostate cancer compared with the unexposed or PDE5i comparison groups. The inverse association between TT and prostate cancer could be the result of residual confounding, contraindication bias, or undefined biological effect.Impact: This study suggests that limited TT exposure does not increase risk of prostate cancer in the short term.

BACKGROUND: Chronic disease is associated with increased health care resource utilization and costs. Effective development and implementation of health care management and clinical intervention programs require an understanding of health plan member enrollment and disenrollment behavior. What this study addsbehavior may provide a valuable context for determining the time frame for the effect of health care programs and initiatives.